



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     :						int	=  {
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE__     :						List[str]	=  [
			    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CLIPSegModel''',
			    '''CLIPSegPreTrainedModel''',
			    '''CLIPSegTextModel''',
			    '''CLIPSegVisionModel''',
			    '''CLIPSegForImageSegmentation''',
			]

if TYPE_CHECKING:
			from .configuration_clipseg import (
			    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    CLIPSegConfig,
			    CLIPSegTextConfig,
			    CLIPSegVisionConfig,
			)
			from .processing_clipseg import CLIPSegProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_clipseg import (
						    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CLIPSegForImageSegmentation,
						    CLIPSegModel,
						    CLIPSegPreTrainedModel,
						    CLIPSegTextModel,
						    CLIPSegVisionModel,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''

import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
SCREAMING_SNAKE_CASE__     :						Any	=  get_tests_dir('''fixtures/vocab.json''')
SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  get_tests_dir('''fixtures''')

class 							a__( unittest.TestCase     ):
			a_	: Union[str, Any]									=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =0






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaConfig()
							snake_case__	     =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)

							# save in new folder
							model_config.save_pretrained(_UpperCAmelCase						)
							processor.save_pretrained(_UpperCAmelCase						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					with tempfile.TemporaryDirectory() as tmpdirname:
							# copy relevant files
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)						)
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   'vocab.json'						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaFeatureExtractor()
							snake_case__	     =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

							snake_case__	     =WavaVecaProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							# save in new folder
							processor.save_pretrained(_UpperCAmelCase						)

							# drop `processor_class` in tokenizer
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'r'						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
									config_dict.pop('processor_class'						)

							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write(json.dumps(_UpperCAmelCase						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaFeatureExtractor()
							snake_case__	     =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

							snake_case__	     =WavaVecaProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							# save in new folder
							processor.save_pretrained(_UpperCAmelCase						)

							# drop `processor_class` in feature extractor
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'r'						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
									config_dict.pop('processor_class'						)

							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write(json.dumps(_UpperCAmelCase						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaConfig(processor_class='Wav2Vec2Processor'						)
							model_config.save_pretrained(_UpperCAmelCase						)
							# copy relevant files
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   'vocab.json'						)						)
							# create emtpy sample processor
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write('{}'						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							int:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)

					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
					self.assertTrue(processor.special_attribute_present						)
					self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)

					snake_case__	     =processor.feature_extractor
					self.assertTrue(feature_extractor.special_attribute_present						)
					self.assertEqual(feature_extractor.__class__.__name__					,   'NewFeatureExtractor'						)

					snake_case__	     =processor.tokenizer
					self.assertTrue(tokenizer.special_attribute_present						)
					if is_tokenizers_available():
							self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'						)

							# Test we can also load the slow version
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase					,   use_fast=_UpperCAmelCase						)
							snake_case__	     =new_processor.tokenizer
							self.assertTrue(new_tokenizer.special_attribute_present						)
							self.assertEqual(new_tokenizer.__class__.__name__					,   'NewTokenizer'						)
					else:
							self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoFeatureExtractor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							AutoTokenizer.register(_UpperCAmelCase					,   slow_tokenizer_class=_UpperCAmelCase						)
							AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(_UpperCAmelCase						):
									AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)

							# Now that the config is registered, it can be used as any other config with the auto-API
							snake_case__	     =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__	     =os.path.join(_UpperCAmelCase					,   'vocab.txt'						)
									with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
									snake_case__	     =CustomTokenizer(_UpperCAmelCase						)

							snake_case__	     =CustomProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(_UpperCAmelCase						)
									snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)
									self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 						_lowercase  (     self						)							->							Union[str, Any]:

					class 							a__( snake_case__     ):
								a_	: Optional[int]									=      False





					class 							a__( snake_case__     ):
								a_	: List[Any]									=      False





					class 							a__( snake_case__     ):
								a_	: int									=      '''AutoFeatureExtractor'''
								a_	: int									=      '''AutoTokenizer'''
								a_	: List[str]									=      False

					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoFeatureExtractor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							AutoTokenizer.register(_UpperCAmelCase					,   slow_tokenizer_class=_UpperCAmelCase						)
							AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# If remote code is not set, the default is to use local classes.
							snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertFalse(processor.special_attribute_present						)
							self.assertFalse(processor.feature_extractor.special_attribute_present						)
							self.assertFalse(processor.tokenizer.special_attribute_present						)

							# If remote code is disabled, we load the local ones.
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertFalse(processor.special_attribute_present						)
							self.assertFalse(processor.feature_extractor.special_attribute_present						)
							self.assertFalse(processor.tokenizer.special_attribute_present						)

							# If remote is enabled, we load from the Hub.
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertTrue(processor.special_attribute_present						)
							self.assertTrue(processor.feature_extractor.special_attribute_present						)
							self.assertTrue(processor.tokenizer.special_attribute_present						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'						)
					self.assertEqual(processor.__class__.__name__					,   'BertTokenizerFast'						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'						)
					self.assertEqual(processor.__class__.__name__					,   'ConvNextImageProcessor'						)





@is_staging_test
class 							a__( unittest.TestCase     ):
			a_	: Dict									=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






			@classmethod
			def 						_lowercase  (     cls						)							->							str:
					snake_case__	     =TOKEN
					HfFolder.save_token(_UpperCAmelCase						)






			@classmethod
			def 						_lowercase  (     cls						)							->							int:
					try:
							delete_repo(token=cls._token					,   repo_id='test-processor'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='valid_org/test-processor-org'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='test-dynamic-processor'						)
					except HTTPError:
							pass






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =WavaVecaProcessor.from_pretrained(_UpperCAmelCase						)
					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(_UpperCAmelCase					,   'test-processor'						)					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token						)

							snake_case__	     =WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""						)
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(_UpperCAmelCase					,   getattr(new_processor.feature_extractor					,   _UpperCAmelCase						)						)
							self.assertDictEqual(new_processor.tokenizer.get_vocab()					,   processor.tokenizer.get_vocab()						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =WavaVecaProcessor.from_pretrained(_UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(_UpperCAmelCase					,   'test-processor-org'						)					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token					,   organization='valid_org'					,   )

							snake_case__	     =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'						)
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(_UpperCAmelCase					,   getattr(new_processor.feature_extractor					,   _UpperCAmelCase						)						)
							self.assertDictEqual(new_processor.tokenizer.get_vocab()					,   processor.tokenizer.get_vocab()						)






			def 						_lowercase  (     self						)							->							Any:
					CustomFeatureExtractor.register_for_auto_class()
					CustomTokenizer.register_for_auto_class()
					CustomProcessor.register_for_auto_class()

					snake_case__	     =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case__	     =os.path.join(_UpperCAmelCase					,   'vocab.txt'						)
							with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
							snake_case__	     =CustomTokenizer(_UpperCAmelCase						)

					snake_case__	     =CustomProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							create_repo(f"""{USER}/test-dynamic-processor"""					,   token=self._token						)
							snake_case__	     =Repository(_UpperCAmelCase					,   clone_from=f"""{USER}/test-dynamic-processor"""					,   token=self._token						)
							processor.save_pretrained(_UpperCAmelCase						)

							# This has added the proper auto_map field to the feature extractor config
							self.assertDictEqual(
							    processor.feature_extractor.auto_map					,   {
							        'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
							        'AutoProcessor': 'custom_processing.CustomProcessor',
							    }					,   )

							# This has added the proper auto_map field to the tokenizer config
							with open(os.path.join(_UpperCAmelCase					,   'tokenizer_config.json'						)						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
							self.assertDictEqual(
							    tokenizer_config['auto_map']					,   {
							        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
							        'AutoProcessor': 'custom_processing.CustomProcessor',
							    }					,   )

							# The code has been copied from fixtures
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_feature_extraction.py'						)						)						)
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_tokenization.py'						)						)						)
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_processing.py'						)						)						)

							repo.push_to_hub()

					snake_case__	     =AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""					,   trust_remote_code=_UpperCAmelCase						)
					# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
					self.assertEqual(new_processor.__class__.__name__					,   'CustomProcessor'						)




